Generative AI is revolutionizing healthcare by enhancing data analysis, clinical note summarization, and insight generation to optimize patient outcomes. Despite its potential, significant privacy and security challenges arise, particularly concerning Protected Health Information (PHI) and intellectual property. To securely deploy medical large language models (LLMs), organizations can leverage Amazon Web Services (AWS) Nitro Enclaves, which offer isolated and verified environments for processing sensitive data. This solution employs AWS Key Management Service (KMS) for model encryption and integrates Amazon Simple Storage Service (S3) for artifacts storage. Moreover, Amazon Simple Queue Service (SQS) facilitates secure query delivery, ensuring private, compliant inferences while preserving model integrity. In a comprehensive setup, organizations can deploy foundational models such as Google MedGemma 4B within Nitro Enclaves, ensuring a trust-centric pipeline for healthcare applications. This framework notably emphasizes the necessity of robust security measures and regulatory compliance for real-world applications, providing a secure pathway to generative AI deployment in healthcare.
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